Antimicrobial peptides (AMPs) are promising candidates in the fight against multidrug-resistant pathogens owing to AMPs’ broad range of activities and low toxicity. Nonetheless, identification of AMPs through wet-lab experiments is still expensive and time consuming. Here, we propose an accurate computational method for AMP prediction by the random forest algorithm. The prediction model is based on the distribution patterns of amino acid properties along the sequence. Using our collection of large and diverse sets of AMP and non-AMP data (3268 and 166791 sequences, respectively), we evaluated 19 random forest classifiers with different positive:negative data ratios by 10-fold cross-validation. Our optimal model, AmPEP with the 1:3 data ratio, showed high accuracy (96%), Matthew’s correlation coefficient (MCC) of 0.9, area under the receiver operating characteristic curve (AUC-ROC) of 0.99, and the Kappa statistic of 0.9. Descriptor analysis of AMP/non-AMP distributions by means of Pearson correlation coefficients revealed that reduced feature sets (from a full-featured set of 105 to a minimal-feature set of 23) can result in comparable performance in all respects except for some reductions in precision. Furthermore, AmPEP outperformed existing methods in terms of accuracy, MCC, and AUC-ROC when tested on benchmark datasets.

Antimicrobial peptides (AMPs) are a valuable source of antimicrobial agents and a potential solution to the multi-drug resistance problem. In particular, short-length AMPs have been shown to have enhanced antimicrobial activities, higher stability, and lower toxicity to human cells. We present a shortlength (%30 aa) AMP prediction method, Deep-AmPEP30, developed based on an optimal feature set of PseKRAAC reduced amino acids composition and convolutional neural network. On a balanced benchmark dataset of 188 samples, Deep-AmPEP30 yields an improved performance of 77% in accuracy, 85% in the area under the receiver operating characteristic curve (AUC-ROC), and 85% in area under the precisionrecall curve (AUC-PR) over existing machine learning-based methods. To demonstrate its power, we screened the genome sequence of Candida glabrata-a gut commensal fungus expected to interact with and/or inhibit other microbes in the gut-for potential AMPs and identified a peptide of 20 aa (P3, FWELWKFLKSLWSIFPRRRP) with strong anti-bacteria activity against Bacillus subtilis and Vibrio parahaemolyticus. The potency of the peptide is remarkably comparable to that of ampicillin. Therefore, Deep-AmPEP30 is a promising prediction tool to identify short-length AMPs from genomic sequences for drug discovery. Our method is available at https://cbbio.cis.um.edu.mo/AxPEP for both individual sequence prediction and genome screening for AMPs.

In human cells, one-third of all polypeptides enter the secretory pathway at the endoplasmic reticulum (ER). The specificity and efficiency of this process are guaranteed by targeting of mRNAs and/or polypeptides to the ER membrane. Cytosolic SRP and its receptor in the ER membrane facilitate the cotranslational targeting of most ribosome-nascent precursor polypeptide chain (RNC) complexes together with the respective mRNAs to the Sec61 complex in the ER membrane. Alternatively, fully synthesized precursor polypeptides are targeted to the ER membrane post-translationally by either the TRC, SND, or PEX19/3 pathway. Furthermore, there is targeting of mRNAs to the ER membrane, which does not involve SRP but involves mRNA- or RNC-binding proteins on the ER surface, such as RRBP1 or KTN1. Traditionally, the targeting reactions were studied in cell-free or cellular assays, which focus on a single precursor polypeptide and allow the conclusion of whether a certain precursor can use a certain pathway. Recently, cellular approaches such as proximity-based ribosome profiling or quantitative proteomics were employed to address the question of which precursors use certain pathways under physiological conditions. Here, we combined siRNA-mediated depletion of putative mRNA receptors in HeLa cells with label-free quantitative proteomics and differential protein abundance analysis to characterize RRBP1- or KTN1-involving precursors and to identify possible genetic interactions between the various targeting pathways. Furthermore, we discuss the possible implications on the so-called TIGER domains and critically discuss the pros and cons of this experimental approach.

The Mycobacterium ulcerans exotoxin, mycolactone, is an inhibitor of co-translational translocation via the Sec61 complex. Mycolactone has previously been shown to bind to, and alter the structure of the major translocon subunit Sec61α, and change its interaction with ribosome nascent chain complexes. In addition to its function in protein translocation into the ER, Sec61 also plays a key role in cellular Ca2+ homeostasis, acting as a leak channel between the endoplasmic reticulum (ER) and cytosol. Here, we have analysed the effect of mycolactone on cytosolic and ER Ca2+ levels using compartment-specific sensors. We also used molecular docking analysis to explore potential interaction sites for mycolactone on translocons in various states. These results show that mycolactone enhances the leak of Ca2+ ions via the Sec61 translocon, resulting in a slow but substantial depletion of ER Ca2+. This leak was dependent on mycolactone binding to Sec61α because resistance mutations in this protein completely ablated the increase. Molecular docking supports the existence of a mycolactone-binding transient inhibited state preceding translocation and suggests mycolactone may also bind Sec61α in its idle state. We propose that delayed ribosomal release after translation termination and/or translocon ‘breathing' during rapid transitions between the idle and intermediate-inhibited states allow for transient Ca2+ leak, and mycolactone's stabilisation of the latter underpins the phenotype observed.

In human cells, approximately 30% of all polypeptides enter the secretory pathway at the level of the endoplasmic reticulum (ER). This process involves cleavable amino-terminal signal peptides (SPs) or more or less amino-terminal transmembrane helices (TMHs), which serve as targeting determinants, at the level of the precursor polypeptides and a multitude of cytosolic and ER proteins, which facilitate their ER import. Alone or in combination SPs and TMHs guarantee the initial ER targeting as well as the subsequent membrane integration or translocation. Cytosolic SRP and SR, its receptor in the ER membrane, mediate cotranslational targeting of most nascent precursor polypeptide chains to the polypeptide-conducting Sec61 complex in the ER membrane. Alternatively, fully-synthesized precursor polypeptides and certain nascent precursor polypeptides are targeted to the ER membrane by either the PEX-, SND-, or TRC-pathway. Although these targeting pathways may have overlapping functions, the question arises how relevant this is under cellular conditions and which features of SPs and precursor polypeptides determine preference for a certain pathway. Irrespective of their targeting pathway(s), most precursor polypeptides are integrated into or translocated across the ER membrane via the Sec61 channel. For some precursor polypeptides specific Sec61 interaction partners have to support the gating of the channel to the open state, again raising the question why and when this is the case. Recent progress shed light on the client spectrum and specificities of some auxiliary components, including Sec62/Sec63, TRAM1 protein, and TRAP. To address the question which precursors use a certain pathway or component in intact human cells, i.e., under conditions of fast translation rates and molecular crowding, in the presence of competing precursors, different targeting organelles, and relevant stoichiometries of the involved components, siRNA-mediated depletion of single targeting or transport components in HeLa cells was combined with label-free quantitative proteomics and differential protein abundance analysis. Here, we present a summary of the experimental approach as well as the resulting differential protein abundance analyses and discuss their mechanistic implications in light of the available structural data.